Exploring Women's Safety: Uncovering Crime Patterns through Fuzzy Clustering Methods | Chapter 3 | Research and Applications Towards Mathematics and Computer Science Vol. 6

 The increasing frequency of crimes against wives worldwide underscores the necessary to identify and implement effective measures to check such occurrence. In order to protect women from cheat, it is necessary to analyze misdeed trends systematically to determine patterns that can serve as valuable precautionary measures. In existing studies focused on crime study, conventional hard assembling techniques are commonly working to assess the prevalence of criminal endeavors in specific regions. However, these grouping methods, grounded in fresh set theory, encounter challenges in handling biased membership, making it troublesome to identify regions presenting partial affiliation accompanying multiple clusters characterized by variable crime intensities. Recognizing the constraints guide hard clustering techniques, we aim to engage a fuzzy grouping approach to a dataset related to crimes against daughters, with the objective of elucidating important patterns within the data.

Author(s) Details:

Samarjit Das,
CSE, The Assam Royal Global University, Guwahati, India.

Atowar Ul Islam,
Department of Computer Science, University of Science & Technology, Ri-Bhoi, Meghalaya, India.

Anupam Das,
CSE, The Assam Royal Global University, Guwahati, India.

Please see the link here: https://stm.bookpi.org/RATMCS-V6/article/view/12522

Behavior of Rounded and Truncated Rand Function Patterns with Increasing and Decreasing Non-normalized Decimal Samples | Chapter 3 | Research Highlights in Mathematics and Computer Science Vol. 8

 In this report research suggestion it may extract few patterns that might accept delivery of something truncated and curved decimal numbers from or by utilizing Rand Excel function as random numbers and Random numbers in SPSS detail of action software. Applying normalization for few samples of Rand decimals numbers as having ten of something data samples. Normalization is worked out for this samples where the arrangement secondhand is by increasing and curbing this random unit of the mathematical system numbers before normalization as non-normalized data and following in position or time applying unit of the mathematical system scale normalization. The samples number that may use is in addition to 20 samples as average and it may inferior this numbers by 5 and more than 20 by 30 and ask decimal scale normalization in MS Excel and SPSS spreadsheet. Could deduct the distinctnesses also 'tween MS Excel and SPSS outputs random numbers and more could receive patterns form using that arrangement (increasing and diminishing samples) for normalized and non-normalized decimal numbers.

Author(s) Details:

Zahir Saif Al-Hashami,
Oman-Muscat, Oman.

Please see the link here: https://stm.bookpi.org/RHMCS-V8/article/view/10191

Effectiveness of Lip Print and Fingerprint Patterns for Gender Determination | Chapter 7 | Research Developments in Medicine and Medical Science Vol. 4

 The most reliable and recognized form of individual identification evidence in a legal tribunal to date is without a doubt dab evidence. Although cheiloscopy is also used to identify feminine, unlike fingerprinting, skilled is still disagreement among investigators as to in any case it is a reliable method. This division aims to determine sexuality of an individual from (1) lip print patterns, (2) fingerprint patterns, and (3) contrasting of the two means in sex determination.Lip prints and abandoned thumb finger prints were got from 89 study participants (43 males and 46 women). The obtained insolence prints and fingerprint patterns were analyzed and statistically judged.Lip print analysis outperformed fingerprints in conditions of specificity (69.6%), positive predicting value (68.9%), and negative predicting value (72.7%) for gender labeling.Lip print patterns proved expected a simple, reliable, and valuable form for sex perseverance, boosting forensic odontologists' duty in criminal investigations.

Author(s) Details:

Ankita Tandon,
Department of Oral Pathology, Microbiology and Forensic Odontology, Dental College, Rajendra Institute of Medical Sciences, Ranchi-834009, Jharkhand, India.

Keya Sircar,
Department of Oral Pathology and Microbiology, Faculty of Dentistry, Jamia Millia Islamia, New Delhi, India.

Aman Chowdhry,
Department of Oral Pathology and Microbiology, Faculty of Dentistry, Jamia Millia Islamia, New Delhi, India.

Deepika Bablani Popli,
Department of Oral Pathology and Microbiology, Faculty of Dentistry, Jamia Millia Islamia, New Delhi, India.

Please see the link here: https://stm.bookpi.org/RDMMS-V4/article/view/9836